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Credit Risk Technology – Risk Data Integration Engineer

Job in Thousand Oaks, Ventura County, California, 91362, USA
Listing for: Mizuho Bank, Ltd.
Full Time position
Listed on 2026-05-31
Job specializations:
  • IT/Tech
    Data Engineering, Data Security, Data Warehousing
Salary/Wage Range or Industry Benchmark: 60000 - 80000 USD Yearly USD 60000.00 80000.00 YEAR
Job Description & How to Apply Below

Join Mizuho as a Credit Risk Technology – Risk Data Integration Engineer!

Credit Risk Technology at Mizuho Americas is seeking a senior Counter party Credit Risk (CCR) Data Integration Engineer to assume end‑to‑end accountability for stable, resilient, and observable data integration pipelines across CCR technology platforms.

In this role, you will be responsible for designing, building, integrating, and operating robust and scalable data architecture, model, and pipelines that support Exposure Management (EM), Credit Risk Reporting (CRR), Risk Analytics, Front Office users, and Regulatory Reporting. The position plays an essential role in delivering reliable data transformation processes for exposure calculation, limit monitoring, and intraday what‑if analysis capabilities across OTC, Exchange‑Traded Derivatives (ETD), and Securities Financing Transactions (SFT).

This role focuses on integrating trade, counter party, collateral, and market data into curated risk data layers used for exposure calculation, limit monitoring, stress testing, and regulatory reporting.

The successful candidate will lead by example, work with a strong ownership/proposal mindset, and drive a technology modernization program to transform the legacy platform into the high‑availability, high‑performance, robust, scalable analysis platform with automated controls for data validation, data completeness, and traceable data lineage.

The ideal candidate must be a seasoned data integration engineer who is technically proficient, able to multi‑task effectively, and capable of clearly articulating complex problems and solutions. The role requires a collaborative, can‑do attitude and the ability to align day‑to‑day execution with a broader strategic vision.

Major Responsibilities
  • Design, develop, maintain and optimize scalable data pipelines to ingest, transform, and integrate counter party, trade, collateral and market data from upstream systems (Systems Of Record) for CCR exposure analytics (e.g., EPE, PFE, EAD, sensitivities).
  • Implement robust ETL/ELT solutions for structured and semi‑structured data across batch and streaming processes using enterprise data platforms (e.g., data lakes, data warehouses, and distributed processing frameworks).
  • Support integration of trading and derivatives data (e.g., exposures, collateral, netting, margin) into CCR calculation and reporting platforms.
Counter party Credit Risk Support
  • Partner with EM and CRR users and business analysts to understand business requirements related to exposure, PFE, EAD, and stress testing.
  • Enable accurate, consistent, validated CCR data for EPE/PFE calculations, limit monitoring, what‑if pre‑trade intraday analysis, and regulatory and HO reporting.
  • Deliver timely enhancements to ETL pipelines, CCR data models, data lineage, and controls aligned with the EM users’ and regulatory expectations (e.g., Basel regulations).
Data Quality, Controls & Governance
  • Implement data quality checks, reconciliations, and monitoring to ensure completeness, accuracy, and timeliness of CCR data (Data governance and compliance).
  • Proactively identify and execute opportunities for process standardization/optimization, tooling enhancements, and operational simplification across the CCR application stack.
  • Lead by example in technical documentation, knowledge transfer, implementation standards, data management, and control frameworks, reducing dependency on key individuals and ensuring service quality, productivity, and continuous skill development.
Incident, Problem & Change Management
  • Proactively investigate, identify root cause of recurring incidents, and resolve data issues across upstream and downstream systems, working with IT and business stakeholders.
  • Demonstrate a continuous improvement mindset through their example, with a focus on reducing incidents, manual interventions, and operational risk, and improving turnaround time for incidents.
  • Drive root cause analysis (RCA) for impactful incidents (Calculation breaks, data validation/reconciliation issues), ensuring recurring issues are eliminated through permanent fixes rather than short‑term workarounds.
  • Enforce robust change and release governance…
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